Comparison of human consensus ratings and M.E.D.V.I.S. algorithm classifications for figure quality.

(a) Confusion matrix showing agreement between human majority votes (Good vs. Bad) and algorithm predictions. (b) Per-figure lollipop plot of the proportion of human raters voting Good (horizontal axis), with points colored green when...

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Bibliographic Details
Main Author:	Hector Torres (11708207) (author)
Other Authors:	Efe Ozturk (16987056) (author), Zhou Fang (209353) (author), Nicholas Zhang (17836430) (author), Shuangyi Cai (22693503) (author), Neel Sarkar (22693506) (author), Ahmet F. Coskun (138133) (author)
Published:	2025
Subjects:	Medicine Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified required background knowledge provide accessible solutions present case studies interpreting complex datasets generate tailored feedback benchmarking visualization platforms biomedical visuals using good &# 8221 standardized evaluation methods iterative scoring algorithm improving figure design evaluating biomedical figures biomedical research classify figures &# 8220 world applicability study introduces quantification system published research introduce spatioview four criteria findings highlight financial cost figure evaluation distinct visualizations developed metrics demonstrate real comprehensive framework comparative analysis color usage
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Comparison of human consensus ratings and M.E.D.V.I.S. algorithm classifications for figure quality.

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